基于LMI的连续非时变不确定系统鲁棒状态估计

研究了连续非时变不确定系统鲁棒状态估计器的基于线性矩阵不等式 ( L MI)解 ,该解建立在业已成熟的 LMI技术基础上 ,形式简单 ,用 MATLAB工具包直接求解。文中还指出了文献 [1 ]的不足 ,最后给出了数值算例 ,以示该方法的有效性。     

不确定线性系统鲁棒跟踪控制器的优化设计

基于优化二次型性能指标,以MATLAB为工具对具有乘性不确定线性系统的鲁棒跟踪控制器的优化设计问题进行研究.以一实例来说明鲁棒跟踪控制器具体的设计方法,给出该不确定系统鲁棒跟踪控制器的参数及系统的跟踪轨迹曲线.结果表明,所设计的鲁棒跟踪控制器能有效的跟踪给定,所提出的设计方法是有效的.     

参数不确定4WS系统的鲁棒H∞控制

针对轮胎参数的不确定性,建立基于轮胎参数变化的汽车四轮转向系统不确定模型,同时进行分析,并将参数不确定性问题转化为基于干扰抑制的H∞控制问题,运用H∞控制理论的分析方法,通过求解Riocati方程设计基于参数变化的动态鲁棒H∞控制器。仿真结果表明所设计的控制器能够很好地抑制轮胎参数的不确定性。     

非线性不确定系统的鲁棒虚拟传感器重组的设计

主要将非线性滑模观测器和虚拟传感器重组方法相结合,设计了具有鲁棒性的滑模观测器。把线性系统虚拟传感器重组方法推广到非线性,并针对传感器故障设计合适的滑模观测器,使改变后的系统能够等价于原来的非线性不确定系统。数字仿真证明了该方法的有效性,重组后的系统输出可以很好地跟踪正常工作系统输出。     

一种参数不确定系统的鲁棒二次保稳定设计方法

研究了不确定线性系统的鲁棒二次保稳定设计问题。在建立的鲁棒回差方程的基础上,给出了闭环系统鲁棒二次保稳定的一个充分条件,以及增益和相位裕度描述形式。进一步还给出了相应的鲁棒二次保稳定控制系统设计方法     

不确定机器人鲁棒自适应控制

针对不确定、时变和非线性机器人系统的实时性要求,提出了采用滑模变结构和RBF神经网络相结合来构造控制器.用带有符号函数的滑模变结构控制器来产生一个控制输入信号,同时利用具有快速学习能力的RBF神经网络来学习外界的不确定性,增强系统的自适应能力,使之达到更佳的控制效果,并在文中证明了系统的稳定性.最后给出了对两连杆机器人的仿真,验证了控制效果.     

基于鲁棒H2滤波的机电BIT虚警抑制方法

针对基于模型的机电控制系统BIT(built-in test, 机内测试)中的虚警问题,分析系统中的信息不确定性和产生虚警的机理;基于鲁棒H2滤波方法设计BIT故障检测诊断系统,该系统对信息不确定性具有较强的鲁棒性,能够充分地抑制虚警.在某机电跟踪与稳定伺服平台BIT系统上进行仿真研究,结果表明机理分析正确,所提方法有效.     

模型不确定压电柔性结构的鲁棒振动控制

以压电柔性结构为对象,考虑其被控模态参数的不确定性和因剔除残余模态所引起的动态不确定性,建立结构的不确定线性分式模型（linear fractional representation）;根据不确定模型,在确保闭环系统H∞范数小于设定上界条件下总体优化也范数,设计对结构进行振动控制的动态输出反馈控制器;并利用线性矩阵不等式（linear matrix inequality）将控制问题转化为具有线性矩阵不等式约束和线性目标函数的凸优化问题。最后以简支压电柔性梁为例,给出仿真结果。     

基于鲁棒保性能滤波的模糊信息融合方法

结合研究存在不确定因素的信息融合问题,提出了一种基于鲁棒保性能滤波的模糊信息融合方法。当系统存在不确定误差时,该信息融合方法能保证系统的融合误差有界,并通过仿真实验显示其有效性。     

订单不确定下双资源约束多装配线鲁棒调度

考虑生产过程中的订单不确定等因素,建立了以最大化交付满意度、最大化装配线平衡率及最小化完工时间跨度为目标的鲁棒调度模型,基于差分进化算法和粒子群算法提出了对模型进行求解的混合优化算法,并通过算例验证了混合优化算法求解该鲁棒调度模型的可行性和有效性。最后综合分析PTCN公司二厂多装配线生产车间的实际生产情况,将所建立的鲁棒调度模型和提出的混合优化算法应用于实际的多装配线生产过程,获得了较优的调度结果。     

一类不确定性时滞系统的鲁棒控制器设计

本文讨论了一类非匹配时滞系统的鲁棒自适应控制和非线性控制问题.所讨论系统包含时滞及不确定性扰动项,且扰动项关于具有未知增益的高阶多项式函数有界而不是关于线性函数有界.利用Lyapunov-Krasovskii方法提出了保证系统渐近稳定的自适应控制器设计方法,相应的控制器是时滞依赖的.并利用Razumikhin引理给出了能使系统一致最终有界稳定的时滞独立的控制器设计方法.     

A new discrete-time robust adaptive time-delay control for a class of uncertain nonlinear strict-feedback systems using sliding mode

This paper presents a new discrete-time adaptive second-order sliding mode control with time delay estimation (TDE) for a class of uncertain nonlinear time-varying strict-feedback systems. The existing researches on time delay control (TDC) are conventionally established based on a stability criterion that is subject to the infinitesimal time delay assumption. Recently, this criterion was rejected and a new criterion was proposed for the development of a controller for systems with fully known dynamics. In this study, this approach is extended to uncertain systems. Specifically, a new criterion is developed for the stability of the TDE-error within an adaptive robust controller design without the infinitesimal time delay assumption. With the proposed adaptive robust control, there is no need for determination of uncertainties upper-bounds. Simulation results illustrate the efficacy of the proposed controller.     

Identification of uncertain nonlinear systems for robust fuzzy control

In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi-Sugeno (T-S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by S?krjanc et al. (2005) [11] and [12]. With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control.     

A numerical investigation for robust stability of fractional-order uncertain systems

This study presents numerical methods for robust stability analysis of closed loop control systems with parameter uncertainty. Methods are based on scan sampling of interval characteristic polynomials from the hypercube of parameter space. Exposed-edge polynomial sampling is used to reduce the computational complexity of robust stability analysis. Computer experiments are used for demonstration of the proposed robust stability test procedures.     

Design of H(infinity) robust fault detection filter for linear uncertain time-delay systems

In this paper, the robust fault detection filter design problem for linear time-delay systems with both unknown inputs and parameter uncertainties is studied. Using a multiobjective optimization technique, a new performance index is introduced, which takes into account the robustness of the fault detection filter against disturbances and sensitivity to faults simultaneously. The reference residual model is then designed based on this performance index to formulate the robust fault detection filter design problem as an H(infinity) model-matching problem. By applying robust H(infinity) optimization control technique, the existence condition of the robust fault detection filter for linear time-delay systems with both unknown inputs and parameter uncertainties is presented in terms of linear matrix inequality formulation, independently of time delay. In order to detect the fault, an adaptive threshold which depends on the inputs is finally determined. An illustrative design example is used to demonstrate the validity of the proposed approach.     

Dissipative proportional integral observer for a class of uncertain nonlinear systems

For a class of uncertain nonlinear systems with sector-bounded nonlinearity, a proportional-integral state observer is designed in a dissipativity framework. The dissipativity ensures the bounded estimation errors against the bounded disturbance and an integral term in the observer structure can provide additional degrees of freedom in the observer design that can be used to improve the estimation performance. In this study, a dissipative condition for the proposed observer is found in terms of a linear matrix inequality (LMI). In order to specify the dissipativity, the L2 gain between the disturbance and the weighted sum of estimation errors is adopted as the supply rate in the dissipativity framework. In addition, the notion of the proposed design method is extended to the exponential dissipativity. The effectiveness of the proposed observer is demonstrated through a numerical example.     

Improved delay-range-dependent robust stability for uncertain systems with interval time-varying delay

This paper is concerned with the improved delay-range-dependent stability and robust stability criteria for linear systems with time-varying delay and norm-bounded uncertainties. In order to obtain much less conservative criteria, a Lyapunov-Krasovskii functional (LKF), which makes use of the information of both the lower and upper bounds of the interval time-varying delay, is proposed to derive new stability criteria. By using delayed decomposition approach (DDA), a tighter upper bound of the derivative of Lyapunov functional can be obtained, and thus the proposed criteria give results with less conservatism compared with some previous ones. The resulting criteria have advantages over some previous ones in that it involves fewer matrix variables but has less conservatism, which are established theoretically. We show, by four well known examples, that our result overcomes the previous allowable maximum admissible upper bound (MAUB) of the time-delay and it is less conservative than the previous results having a relatively small upper bound in the derivative of time delay.     

Adaptive fuzzy approach for a class of uncertain nonlinear systems in strict-feedback form

Adaptive fuzzy control is proposed for a class of affine nonlinear systems in strict-feedback form with unknown nonlinearities. The unknown nonlinearities include two types of nonlinear functions: one satisfies the "triangularity condition" and can be directly approximated by fuzzy logic system, while the other is assumed to be partially known and consists of parametric uncertainties. Takagi-Sugeno type fuzzy approximators are used to approximate unknown system nonlinearities and the design procedure is a combination of adaptive backstepping and generalized small gain design techniques. It is proved that the proposed adaptive control scheme can guarantee the uniformly ultimately bounded (UBB) stability of the closed-loop systems. Simulation studies are shown to illustrate the effectiveness of the proposed approach.